OBJECTIVES: While studies have documented racial and ethnic disparities in amputation rates for patients with peripheral artery disease (PAD), the importance of specific factors has not been quantified. This research seeks to provide such evidence, and to quantify how much of the difference reflects observable versus unexplained factors.    METHODS: This study used the nationally-representative HCUP inpatient database from 2006-2013 for patients with a primary diagnosis of PAD.   Amputation rates were calculated for major and minor amputations. Logistic regression models were estimated to identify the determinants of amputation rates. All models were adjusted for patient demographics, income strata, comorbidities, risk factors and hospital characteristics. The Blinder-Oaxaca decomposition method was employed to ascertain and quantify the factors that contribute to the racial/ethnic disparities.  RESULTS: Caucasians are generally older, wealthier, have milder disease and a lower mortality risk than either African-Americans or Hispanics.   Multivariable results revealed that African-Americans and Hispanics are about twice as likely to be amputated as are Caucasians.  Observed factors in the models collectively account for 50% to 55% of the disparities for African-Americans and 58% to 70% for Hispanics.  The most important factors being higher mortality risk, disease severity, and a higher rate of ER admission.  This suggests that African-Americans and Hispanics have less access to care, because they are being admitted when sicker and more likely on an emergent basis. CONCLUSIONS: Racial and ethnic disparities in amputation rates are substantial, with disease severity and hospital admission source being key factors.  As the population ages and comorbidities rise, these disparities may accelerate unless access among minorities improves.  Moreover, because some 30% to 50% of these disparities remain unexplained by access or other observed factors, substantial disparities could persist despite improvements in access. Identifying the precise factors causing these large unexplained variations is an important direction for further study.